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Taxonomic and Functional Shifts in Sprout Spent Irrigation Water Microbiome in 1

Response to Salmonella Contamination of Alfalfa Seeds 2

Jie Zhenga#, Elizabeth Reeda, Padmini Ramachandrana, Andrea Ottesena,c, Eric W. Browna, Yu 3

Wangb 4

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aDivision of Microbiology, Office of Regulatory Science, Center for Food Safety and Applied 6

Nutrition, U.S. Food and Drug Administration, 5001 Campus Drive, College Park, MD 20740 7

bBiostatistics & Bioinformatics Staff, Office of Analytics and Outreach, Center for Food Safety 8

and Applied Nutrition, U.S. Food and Drug Administration, 5001 Campus Drive, College Park, 9

MD 20740 10

cpresent address: Division of Animal and Food Microbiology, Center for Veterinary Medicine, 11

U.S. Food and Drug Administration, 8301 Muirkirk Rd, Laurel, MD 20708 12

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#Corresponding author’s e-mail: jie.zheng@fda.hhs.gov 14

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Running title: SSIW microbiome and Salmonella interaction 17

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AEM Accepted Manuscript Posted Online 20 November 2020Appl Environ Microbiol doi:10.1128/AEM.01811-20This is a work of the U.S. Government and is not subject to copyright protection in the United States.Foreign copyrights may apply.

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ABSTRACT 19

Despite recent advances in Salmonella-sprout research, little is known about the relationship 20

between Salmonella and the sprout microbiome during sprouting. Sprout spent irrigation water 21

(SSIW) provides an informative representation of the total microbiome of this primarily 22

aquaponic crop. This study was designed to characterize the function and taxonomy of the most 23

actively transcribed genes in SSIW from Salmonella Cubana contaminated alfalfa seeds 24

throughout the sprouting process. Genomic DNA and total RNA from SSIW was collected at 25

regular intervals and sequenced using Illumina Miseq and NextSeq platforms. Nucleic acid data 26

were annotated using four different pipelines. Both metagenomic and metatranscriptomic 27

analyses revealed a diverse and highly dynamic SSIW microbiome. A ‘core’ SSIW microbiome 28

comprised Klebsiella, Enterobacter, Pantoea, and Cronobacter. The impact, however, of 29

Salmonella contamination on alfalfa seeds influenced SSIW microbial community dynamics not 30

only structurally but also functionally. Changes in genes associated with metabolism, genetic 31

information processing, environmental information processing, and cellular processes were 32

abundant and time dependent. At timepoints of 24hrs, 48hrs, and 96hrs, a total of 541, 723, and 33

424 S. Cubana genes, respectively, were transcribed at either higher or lower levels compared 34

with S. Cubana at 0hr in SSIW during sprouting. An array of S. Cubana genes (107) were 35

induced at all three time points including genes involved in biofilm formation and modulation, 36

stress responses, and virulence and tolerance to antimicrobials. Taken together, these findings 37

expand our understanding of the effect of Salmonella seed contamination on the sprout crop 38

microbiome and metabolome. 39

IMPORTANCE: Interactions of human enteric pathogens like Salmonella with plants and plant 40

microbiomes remain to be elucidated. The rapid development of next generation sequencing 41

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technologies provides powerful tools enabling investigation of such interactions from broader 42

and deeper perspectives. Using metagenomic and metatranscriptomic approaches, this study not 43

only identified the changes in microbiome structure of SSIW associated with sprouting, but also 44

the changes in the gene expression patterns related to the sprouting process in response to 45

Salmonella contamination of alfalfa seeds. This study advances our knowledge on Salmonella-46

plant (i.e. sprouts) interaction. 47

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INTRODUCTION 48

Sprouts have been associated with numerous outbreaks caused by various pathogens 49

including Salmonella enterica and Escherichia coli O157: H7, and a variety of Salmonella 50

enterica serovars have been implicated in Salmonella associated outbreaks linked to sprouts 51

(http://www.outbreakdatabase.com; https://www.cdc.gov/foodsafety/outbreaks/index.html). 52

Although pathogen contamination of sprouts can occur during the production process, seeds are 53

considered to be the most common source of contamination (1, 2). Consistent with this belief, 54

seed contamination has important implications for the contamination cycle of enteric pathogens 55

in the sprout production environment (2). However, pathogens are usually undetectable in seed 56

lots prior to germination (3). Seed sprouting provides an excellent environment for the growth of 57

microorganisms, including foodborne pathogens. A recent study of microbiological quality in 58

retail alfalfa sprouts revealed a distribution of approximately 7.2 – 7.6 log CFU/g of aerobic 59

plate count (APC) (4). Sprout spent irrigation water (SSIW), i.e., the water that has flowed 60

through the sprouts during production, can provide a representative sample of the entire 61

microbial population, including pathogens, in a batch of sprouts. So much so, microbial counts 62

in the SSIW are usually within 1 log of the counts in sprouts (5). It has been recommended as an 63

analytical sample instead of the sprouts themselves, as an important part of a multi-hurdle 64

strategy to enhance sprout safety (6). 65

Factors affecting the growth of Salmonella during sprouting of contaminated seeds have 66

been examined by several research groups (7-10), including initial inoculum level, incubation 67

temperature, length of exposure, contaminated seed storage time, seeds washing frequency, as 68

well as Salmonella serovars and strain virulence. Combined with fluorescence microscopy 69

observations, Barak et al. (11) demonstrated that the curli phenotype played an important role in 70

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the binding of S. enterica to alfalfa sprouts. Additionally, Howard et el. (8) confirmed that S. 71

enterica can grow saprophytically on soluble organics released from seeds during early phases of 72

germination. Salmonella-sprouts and Salmonella-sprout microbiota interactions during the 73

sprouting process, however, are still poorly understood. 74

Transcriptomics and metatranscriptomics have become powerful tools to better understand 75

the process of disease and other complex biological processes such as biofilm formation, stress 76

response, and pathogen-plant interaction (12-14). Unlike transcriptomics, however, 77

metatranscirptomics can capture gene expression patterns in natural microbial communities (15, 78

16). Also different from metagenomics, which provides an inventory of the community gene 79

pool, metatranscriptomics identifies the diversity of the active genes in a given ecological 80

context, including under experimentally manipulated conditions (17, 18). 81

The purpose of this study was to examine the dynamics and functional activity of microbe-82

microbe interactions in spent irrigation water during sprouting of Salmonella contaminated 83

alfalfa seeds by shotgun metagenomic and metatranscriptomic approaches. 84

RESULTS 85

Shot-gun metagenome analysis reveals temporal patterns of microbial diversity in 86

spent sprout irrigation water (SSIW). Shotgun sequencing was performed with spent 87

irrigation water at different time points from alfalfa seeds contaminated with Salmonella enterica 88

serovar Cubana at varying levels (0, 0.2, 2, and 104 cfu/g of seed) and also with a tap water 89

control. Sequencing reads were analyzed for identification of microbial DNA at the species level 90

and determination of the organism’s relative abundance using the CosmosID bioinformatics 91

software package (CosmosID Inc., Rockville, MD). 92

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Shotgun metagenomic analysis revealed a core SSIW microbiome comprising few bacterial 93

genera dominated by Klebsiella, Enterobacter, Pantoea, and Cronobacter, with a strikingly high 94

relative abundance (90.0 ± 6.9%) across all 4hr sampling points and associated inoculation levels, 95

while the tap water control only comprised the genus Afipia (Fig. 1A). As the levels of 96

Salmonella increase, however, a reduction in the relative abundance of Cronobacter can be 97

observed with the exception of the 24hr sample at the 2 cfu/g seed Salmonella inoculation level. 98

The relative abundance of Pantoea decreased drastically to below 10% in the first 24 hours of 99

sprouting at all Salmonella inoculation levels except at the level of 104 cfu/g seed. Salmonella is 100

rarely detectable at concentrations lower than 0.2 cfu per gram of seed across all 4-h and 8-h 101

sampling points (Fig. 1A and 1B), and no significant change was observed in Salmonella 102

populations over the sampling time at Salmonella inoculation levels of 0.2 cfu/g seed or 104 103

cfu/g seed (Fig. 1A and 1B). Interestingly, a peak in Salmonella relative abundance was noted 104

between 32-h and 36-h time points at an inoculation level of 2 cfu/g seed followed by a decrease 105

in Salmonella relative abundance afterwards (Fig. 1A and 1B). It is also notable that with a 106

longer sampling period, an increase in the relative abundance of Pseudomonas after 48 hours 107

was observed (Fig. 1B). 108

Metatranscriptome characteristics and annotation. SSIW without Salmonella 109

inoculation at various sampling points were used as controls to examine the role of Salmonella in 110

SSIW microbial community dynamics during sprouting. After quality control, approximately 111

106.75 million combined metagenomic reads were produced from the Illumina MiSeq, 112

comprising 10.8 billion bp, with an average read length of 101 bp across the 21 samples, ranging 113

from a mean of 2,958,984 to 6,158,197 sequences per replicate samples at each time point (Table 114

1). After filtering out rRNA reads with the SortMeRNA algorithm against the SILVA database 115

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(Microbial Genomics and Bioinformatics Research Group), metatranscriptome datasets with a 116

mean between 2,655,703 and 5,606,201 reads per time point were submitted to the MG-RAST 117

pipeline (Table 1). The process resulted in transcriptional features ranging from 461,101 to 118

1,307,715 per metatranscriptomic dataset at each time point. According to MG-RAST-based 119

lowest common ancestor (LCA) classification of the SSIW metatranscriptomes, 50.55% to 83.67% 120

of the functionally annotated transcriptional features were assigned to Bacteria while 0.02% to 121

0.60% were assigned as ‘unclassified sequences’ due to bacteria (Table 1). A large proportion of 122

the transcriptional features were assigned as ‘unclassified sequences’ due to Plantae from 11.82% 123

to 48.4% per metatranscriptomic dataset at each time point. 124

In another aspect, Salmonella in SSIW at 0h was used as a control to understand the microbial 125

community effect on changes in Salmonella function during the sprouting process. After quality 126

control, between 50,915,442 and 92,305,144 combined metagenomic reads were recovered from 127

replicate samples at each time point with a total of 15 samples from NextSeq 500 system. Of 128

these, 4 – 24% of reads matched the rRNA database and were removed once again with the 129

SortMeRNA algorithm. Metatranscriptome datasets with a mean of between 64,654,545 and 130

72,001,722 reads per time point were submitted to the MG-RAST pipeline (Table 1), and an 131

average of 16,419,988 and 30,841,884 transcriptional features per metatranscriptome dataset at 132

each time point were obtained from the pipeline. A majority of the features were assigned to 133

Bacteria, averaging from 78.97% to 99.81%. However, various proportions of the transcriptional 134

features were assigned as ‘unclassified sequences’ due to Plantae, ranging from 0.00% to 19.05% 135

on average. 136

Taxonomic abundance profiling from SSIW metatranscriptome with four different 137

classification tools. The taxonomic assignments of the metatranscriptomic datasets sequenced 138

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using MiSeq in this study were also predicted at the genus level with the CosmosID, MetaPhlAn, 139

and bactiKmer, in addition to the MG-RAST annotation pipeline. Variations in relative 140

abundance were observed across the samples between biological replicates, and among the 141

different taxa classifiers (Fig. 2). Despite the variation in the relative abundances, the same 142

families appeared as active bacterial members residing in SSIW across almost all the classifiers, 143

except MetaPhlAn. Salmonella, Pantoea, Pseudomonas, Cronobacter, and Enterobacter, 144

together with Bacillus, Erwinia, Paenibacillus and Escherichia were identified with at least two 145

of the classifiers, representing the most active genera in SSIW (Fig. 2). It is noted that some of 146

the tools employed here identified additional non-bacterial genus/species due to differences in 147

their reference databases. For example, MetaPhlAn identified two plant virus species (i.e. peanut 148

stunt virus and alfalfa mosaic virus), while MG-RAST detected two genera of Plantae in the 149

Legume family (Fabaceae), Glycyrrhiza and Medicago, and one genus of Fungi, Mucor (Fig. 2). 150

Changes in SSIW Microbial Community Function associated with S. Cubana seed 151

contamination. Global functional classification of prokaryotic transcriptional features from 152

MiSeq SSIW metatranscriptomic datasets was performed with SEED subsystems in MG-RAST. 153

Among the functional categories identified by MG-RAST, the five most dominant categories 154

based on the relative abundance of assigned reads across all SSIW samples from the control 155

group , and SSIW samples from Salmonella-contaminated seeds, respectively, were: protein 156

metabolism (16.1±4.65% and 15.8±3.67%), clustering-based subsystems (functional coupling 157

evidence but unknown function; 12.8±0.35% and 12.6±0.50%), carbohydrates (11.0±1.91% and 158

11.6±1.91%), , amino acid and derivatives (6.6±1.14% and 6.7±0.99%), and cell wall and 159

capsule (5.8±0.90% and 5.9±0.79%) (Fig. 3A). Comparative analysis of the SSIW communities 160

with and without Salmonella based on the full set of replicates showed the same top-10 most 161

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enriched functional categories between the two microbial communities (Fig. 3A). Moreover, 162

different temporal patterns were observed within the SSIW microbial community. For example, 163

increased relative abundance over time was found in genes related to protein metabolism, and 164

RNA metabolism. In addition, reduced relative abundance over time was found in genes related 165

to DNA metabolism, cell wall and capsule, and fatty acids, lipids, and isoprenoids. Lastly, the 166

relative abundance of genes related to other functions in Fig. 3B displayed “peak” or “valley” 167

patterns over time. However, contamination with S. Cubana did not alter the existing temporal 168

pattern of the genes related to most functional categories in the SSIW microbial community. No 169

effect or even slight effect in the relative abundance was observed when compared to the SSIW 170

control microbiome at different time points. Nevertheless, inoculating alfalfa seeds with S. 171

Cubana did change the temporal patterns in seven functional categories including carbohydrates, 172

clustering-based subsystems, stress response, regulation and cell signaling, motility and 173

chemotaxis, nucleosides and nucleotides, and respiration (Fig. 3C). For instance, compared with 174

the SSIW control microbiome, the increase in relative abundance in genes related to 175

carbohydrates metabolism at 24h changed the dynamics of carbon metabolism in the SSIW-176

Salmonella microbiome. Subsystem level 2 analysis highlighted the role of reads encoding sugar 177

alcohols in the temporal change of overall relative abundance of carbohydrates related reads in 178

the SSIW-Salmonella microbiome (Fig. 3D). The dynamics in relative abundance of annotated 179

reads corresponding to cold shock, osmotic stress, and detoxification contributed largely to the 180

pattern change observed related to stress response (Fig. 3D). It was also noted that the abundance 181

changes in genes associated with quorum sensing and biofilm formation as well as regulation of 182

virulence at different time points may both play an important role in the overall dynamic change 183

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in regulation and cell signaling when comparing the SSIW Salmonella microbiome with SSIW 184

control microbiome. (Fig. 3D). 185

The same SSIW metatranscriptomic datasets were also annotated by comparison with the 186

Kyoto Encyclopedia of Gene and Genomes (KEGG) database in MG-RAST. The top ten most 187

enriched KEGG pathways across all of the samples from Salmonella-contaminated alfalfa seeds 188

during sprouting were identified based on the hits of assigned reads to KEGG orthology (KO) 189

accession numbers when comparing the samples with controls at the same time point. Numbers 190

of differentially regulated genes among the top ten pathways at each time point were grouped 191

based on high level function, i.e. metabolism, genetic information processing, environmental 192

information processing, and cellular processes (Fig. 4). Overall, the effect of Salmonella 193

contamination of alfalfa seeds was dynamic on the SSIW microbial community. In the cellular 194

processes function, flagellar assembly pathways were highly enriched at 24hr with up-regulation 195

of seven genes involved in the assembly. At 48hr, bacterial chemotaxis pathways were 196

significantly enriched with down-regulation of 11 genes involved in the network. Notably, the 197

microbial community was most active at 48hr with pathways involved in metabolism and 198

environmental information processing, while networks within genetic information processing, 199

for instance, homologous recombination, were most active during the first 24 hrs (Fig. 4). 200

Changes in Salmonella Function during Interaction with SSIW Microbial Community. 201

A total of 541, 723, and 424 S. Cubana genes at 24hr, 48hr, and 96hr, respectively, were either 202

upregulated or downregulated by at least twofold (FDR0.05) compared with S. Cubana at 0hr in 203

SSIW during sprouting (Fig. 5, Table S1). Among the three time points sampled during 204

sprouting, most changes in the S. Cubana transcriptome were observed at 48hr. Interestingly, a 205

substantial pool of S. Cubana genes (107) were induced at all three time points including genes 206

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involved with biofilm modulation (bhsA), curli synthesis (csg operon), cellulose biosynthesis 207

(yhjQ), acid adaptation (cad operon), hyperosmotic stress response (osm operon, otsB), 208

superoxide stress response (ibpA), universal stress response (uspF, and uspG), 209

lipopolysaccharide biosynthesis (wzzB, manC), type III secretion effector (slrP), toxin synthesis 210

(ldrD, ltxB), and DNA gyrase inhibitor (sbmC), and a transcriptional regulator yvoA. The 211

transcriptional regulator shared a sequence identity of 38% with the well-studied DasR regulator 212

from the antibiotic-producing soil bacterium Streptomyces, which represents a master switch in a 213

signaling cascade from the nutrient GlcNAc to antibiotic production. However, only three genes 214

were downregulated and shared by all three time points. One of these three genes is metR, 215

encoding a homocysteine-dependent transcriptional activator, which controls methionine 216

biosynthesis and transport (Table S2). It is also important to note that, although some genes were 217

upregulated or downregulated at all sampled times points compared with 0hr, the level of 218

expression exhibited temporal dynamics. For example, while cadABC showed upregulation at 219

all three time points, the level of expression decreased substantially from 24hr to 48hr (Fig. 6), 220

indicative of changes in lysin-dependent acid resistance. Also, a decrease in the level of 221

expressions of osmB, osmX, and osmY after 24hr also suggested changes to the levels of osmotic 222

stress over time (Fig. 6). 223

These differentially expressed genes were further compared with the KEGG database to 224

determine enriched KEGG pathways in S. Cubana at each time point during sprouting. The ten 225

most enriched KEGG pathways in S. Cubana were identified based on the hits of assigned reads 226

to the Salmonella enterica subsp. enterica serovar Cubana KEGG Genes Database in paired 227

comparisons between different time points during sprouting. Numbers of differentially regulated 228

genes among the top-ten pathways at each time point were grouped together based on high level 229

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function (i.e. metabolism, genetic information processing, environmental information processing, 230

and cellular processes) (Fig. 7A). Metabolically, S. Cubana was most active at 48hr, with the 231

greatest number of genes involved in metabolic pathways when compared with 24hr and 96hr. 232

However, the genetic profile involved in metabolic pathways at 48hr was strikingly different 233

from 24hr and 96hr. At 48hr, 75% of the genes involved in metabolic pathways were down-234

regulated, while 69% and 34% of these genes at 24hr and 96hr, respectively, were up-regulated 235

within the networks. Moreover, 63% of genes at 96hr were up-regulated when compared with 236

genes at 48hr. Two-component systems and ABC transporters were the two main networks 237

under the environmental information processing function observed among the top ten most 238

enriched pathways (Fig. 7B). A similar temporal pattern was found in genes associated with two-239

component systems and ABC transporters as in metabolic pathways. However, it is worth 240

mentioning that the greatest number of up-regulated genes was observed at 48hr instead of 24hr 241

in the two-component systems. 242

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DISCUSSION 244

Recent advances in human enteric pathogen – plant interaction insights have provided a 245

better understanding of colonization and persistence of enteric pathogens on and in plant tissues 246

(19-21). However, factors involved in the fitness of enteric pathogens in this ecological niche 247

and their interaction with plants remain to be elucidated. Microbiome profiling of plants has 248

revealed a diverse and highly dynamic plant microbiome, often termed the plant’s “second 249

genome” (22). Several studies have shown that bacterial communities are dynamically shaped by 250

environmental factors as are the members within that community (23-25). Since sprouts are 251

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germinated or partially germinated seeds and traditionally produced entirely in water, the 252

microbial properties of the spent sprout irrigation water (SSIW) should best inform our 253

understanding of the nature and dynamic of the spermosphere (i.e. the short lived, rapidly 254

changing zone of soil/water surrounding a germinating seeds) microbes (26). The dominant 255

spermosphere bacteria can be recruited from the seed endophytic and epiphytic microbiota, or 256

the surrounding water. In this study, the microbial community of SSIW from S. Cubana 257

contaminated alfalfa seeds was profiled using both metagenomic and metatranscriptomic 258

approaches. After analyzing our datasets with the CosmosID bioinformatics platform, several 259

genera from the Enterobacteriaceae family were revealed to comprise the majority SSIW 260

microbiome when compared to tap water controls. Among the taxa, Klebsiella, Enterobacter, 261

Pantoea, Pseudomonas, Paenibacillus and Bacillus are well-known spermosphere bacteria, not 262

only dominating bacterial communities in most soil, but also endophytic and epiphytic seed 263

communities (26, 27). The metatranscriptome captures real-time functional activities of the 264

microbiome, therefore, greater diversity was observed within the same sample and among 265

individual samples in the metatranscriptome. However, both metagenome and metatranscriptome 266

analysis revealed the temporal patterns in the relative abundance of Pantoea Tatumella and 267

Pseudomonas species in the SSIW control community and the changes in the relative abundance 268

of Cronobacter, Klebsiella, and Enterobacter species in the SSIW Salmonella community as 269

Salmonella inoculation levels increased. These data indicated complex microbe-microbe and 270

microbe-seed interactions during sprouting. Additionally, every 8-hour instead of 4-hour 271

sampling did not change the core microbiome and associated temporal pattern as well as 272

Salmonella relative abundance. The 8-hour sampling scheme, however, did greatly increase the 273

diversity of the SSIW microbiome. 274

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Currently, how much seed endophytic species or environment-inhabiting species contribute 275

to the proliferating spermosphere and/or spermoplane microbiota during seed germination is 276

unknown. Matos et al. compared the culturable portion of the native microflora of various types 277

of sprouts and associated physiological profiles to assess the relative effects of sprout type and 278

inoculum factors on the microbial community structure of sprouts (28). Variability among sprout 279

types was found to be more extensive than any differences between microbial communities 280

associated with sprouts from different sprout-growing facilities and seed lots (28). The sprouting 281

environment, however, may play a role on the microbiota of sprouts. Weiss et al. found that 282

dominating cultivable species were different in hydroponically grown sprouts versus soil grown 283

samples (29). Using seeds from the same distributor, similar organismal families were found on 284

all final sprout varieties and were primarily composed of Pseudomonadaceae. However, 285

commercially germinated sprout varieties housed more diverse microbial families than 286

laboratory sterile water-germinated sprouts of all three varieties (30). Asakura et al (31) reported 287

that seasonal and growth-dependent variation of bacterial community structure were observed in 288

radish sprouts using 16S rRNA sequencing analysis. A predominance of Pseudomonas spp. was 289

found throughout seasons with summer samples exhibiting an increase in Enterobacteriaceae 290

and decreases in Oxalobacteraceae and Flavobacteriaceae compared with winter samples. 291

Compared with pre-sprouted seeds, an increased proportion of Pseudomonas spp. was observed 292

after sprouting (31). In this study, after ample comparison to tap water controls, different 293

bacterial species were found to be recruited from alfalfa seeds and dominate the spermosphere at 294

different growth stages of the sprouting, suggesting that the spermosphere microbiota is dynamic, 295

not static, and that microbial effects during seed development and dispersal events may be 296

especially important to the microbiota of alfalfa sprouts. 297

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A suite of software tools has been developed to taxonomically classify metagenomic and 298

metatranscriptomic data and estimate taxon abundance profiles. In this study, four different 299

software tools were used to perform taxonomic classification of the SSIW metatranscriptomic 300

dataset. Among these tools, the CosmosID bioinformatics platform and BactiKmer metagenome 301

pipeline (32) utilized high performance k-mer-based algorithms and curated taxonomy databases 302

(GenBook in CosmosID, Rockville, MD). MetaPhlAn is a computational tool for profiling the 303

composition of microbial communities from metagenomic shotgun sequencing data and relies on 304

unique clade-specific marker genes identified from 3,000 reference genomes (33). The MG-305

RAST automated analysis pipeline uses a DNA-to-protein classifier and the M5nr (MD5-based 306

non-redundant protein database) for annotation (34). In this study, the same metatranscriptomic 307

dataset, after filtering out all the rRNA reads, were subjected to these four different classifiers. It 308

is of note that some variations in the taxonomic assignments revealed in the analytical results 309

were due to the completeness of the pre-compiled database from each software tool. Others 310

might be caused by reclassification of certain genera. For example, some species of Pantoea 311

were transferred to the genus Tatumella (35), and some species of Enterobacter to the genus 312

Kosakonia and Lelliottia (36, 37). In addition, bias was observed using MetaPhlAn as a 313

taxonomic classifer due to an uneven distribution of marker sequences among the microbial 314

sequences of interest (38). Albeit, most databases still remain poorly populated below the species 315

level. Thus, depending on the application, a single or multiple classifier may be chosen. 316

The release of molecules from germinating seeds into the surrounding water generates a 317

rapid explosion of microbial growth and activity in the spermosphere (39-41). Community-level 318

physiological profile (CLPP) analysis also suggested significant changes in the microbial 319

community metabolic diversity during sprouting for alfalfa sprouts (10). Seed exudates are rich 320

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in sugars, amino acids, organic acids among other things (39, 41). Results from early studies (42) 321

that Enterobacter cloacae, a well-known plant endophyte and commonly found in seeds, grew 322

on simple mono- and oligo-saccharides but not on polysaccharides paralleled the proliferation of 323

E. cloacae and the increased monosaccharides and di- and oligosaccharides catabolism in the 324

SSIW microbiome in this study. Moreover, the greater abundance of E. cloacae in the SSIW 325

Salmonella microbiome may explain why contamination of Salmonella in alfalfa seeds did not 326

notably affect seed germination and growth since E. cloacae has been shown to enhance seed 327

gemination and seedling growth (43). Recent studies pointed to sugar alcohols, a class of polyols, 328

as having a role in plant-pathogen interaction. It was observed that the tomato pathogen 329

Cladosporium fulvum produced mannitol to suppress reactive oxygen species (ROS)-mediated 330

plant defenses (44). In addition, mannitol production and secretion by a fungal pathogen of 331

tobacco and numerous other plant species, Alternaria alternata, was massively induced by host 332

plant extracts (44, 45). In this study, the greater abundance of genes associated with sugar 333

alcohol catabolism in the SSIW Salmonella microbiome in the first 24 h during sprouting may 334

suggest a Salmonella - sprout interaction. This result thus may also indicate a Salmonella - 335

microbial community interaction as sugar alcohols may have effects on enzyme activity and 336

microbial community structure (46). Further investigation of the various molecules released 337

during sprouting will help to better understand the roles of exudate molecules in stimulating 338

pathogens and supporting bacterial growth in the spermosphere and also in influencing the 339

interactions that take place in the spermosphere. 340

Within the SSIW microbiome, ecological competition is often intense, particularly among 341

species with overlapping nutrient requirements. The main functional roles of SSIW microbiota 342

are related to nutrient processing, energy production and biosynthesis of various secondary 343

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metabolites as suggested in this study. Therefore, microbial members in the SSIW microbiome 344

are likely subjected to various environmental stressors associated with competition and the host, 345

such as exposure to reactive oxygen species and secondary metabolites released from plant 346

defenses and microbial species in the microbiome. In response, related genes and pathways were 347

observed in high abundance in the metatranscriptome of SSIW with or without Salmonella. 348

Interestingly, increased abundance in oxidative stress, osmotic stress and quorum sensing, and 349

biofilm formation in the SSIW-Salmonella microbiome at different time points was observed 350

when compared with the control microbiome. In addition, down-regulation of antibiotic 351

biosynthesis was observed in the KEGG pathway enrichment analysis. All of these data point to 352

the notion that Salmonella induces stress response, biofilm formation, and antibiotic tolerance in 353

this ecological niche in response to the host and its own competitors, as suggested in a very 354

recent study by B. Lories et al (47). 355

This study also used a metatranscriptomic approach to examine the SSIW microbiome with 356

and without Salmonella. KEGG pathway enrichment analysis suggested that inoculation of S. 357

Cubana in alfalfa seeds has altered the function of the SSIW microbial community in metabolism, 358

environmental information processing, and cellular processes. For example, gene functions 359

related to membrane transport and signal transduction (i.e., two-component systems) were highly 360

enriched, suggesting dynamic Salmonella-SSIW microbiome and Salmonella-sprout interactions. 361

Moreover, these enriched pathways recruited the greatest number of genes at 48hr, and the least 362

number of genes at 96hr, supporting temporal dynamic interactions in the SSIW microbiome. In 363

addition, KEGG pathway enrichment analysis in both the metatranscriptome datasets supported 364

temporal change in the regulation of genes related to flagellar assembly and bacterial chemotaxis 365

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pathways under cellular processes, suggesting a motility-to-biofilm transition in the SSIW-366

Salmonella microbial community in the first 48 hours of sprouting (48). 367

In a previous study, Kerstin Brankatschk et al. (49) examined S. Weltevreden interaction 368

with alfalfa sprouts during colonization using RNA-seq. In comparison with a M9-glucose 369

medium, the study showed that 177 genes (4.3% of S. Weltevreden genome) were transcribed at 370

higher levels with sprouts, including the genes coding for proteins involved in attachment, 371

motility, biofilm formation, and proteins of Salmonella pathogenicity island 2, clearly 372

demonstrating some of the commonality shared between bacterial infection of plants and humans. 373

The main caveats of this study were that Salmonella was only inoculated on 5-day old sprouts 374

and the use of S. Weltevreden in the M9-glucose medium for comparison. In the present study, 375

which focuses solely on SSIW, the sprouting process was followed for several days, and the 376

transcriptome profile of S. Cubana at different time points was compared to S. Cubana at 0hr in 377

SSIW during sprouting. One possible explanation for this difference may be that this study 378

focused solely on SSIW and not sprouts. Differential gene expression and pathway enrichment 379

analyses showed a marked shift in major transcriptional activities to metabolism and 380

environmental information processing such as two-component systems and ABC transporters, 381

suggesting a dynamic interaction of S. Cubana with the SSIW microbial community. Previously, 382

a shift in the expression pattern of various metabolic pathways was also found in Escherichia 383

coli O157:H7 exposed to lettuce leaves or leaf lysates (50, 51). Constitutive up-regulation of the 384

transcriptional regulator yvoA (52) and downregulation of homocysteine-dependent 385

transcriptional activator genes metR (53) further suggests their contribution to metabolism shifts 386

in S. Cubana. Moreover, genes that are transcribed at higher levels at all three time points 387

sampled during sprouting shed more light onto the survival and adaptation mechanisms of S. 388

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Cubana in the SSIW microbiome. S. Cubana enhanced stress response to oxidative species, 389

hyperosmostic stress, and other stresses by inducing genes including the cad and osm operons, 390

otsB, ibpA, uspF, and uspG (54-57). In response to limited nutrients, in addition to altered 391

metabolism, S. Cubana induced biofilm formation by upreglation of bhsA, the csg operon, and 392

yhjQ (58, 59). In response to the competitors in the SSIW microbiome, S. Cubana increased 393

virulence (slrP, manC), antibiotic and toxin production (yvoA, ldrD, and ltxB) and resistance to 394

antimicrobials produced by other competitors (sbmC) (60-64). 395

In summary, all of these data demonstrated that the addition of Salmonella in the 396

spermosphere environment may help reshape the SSIW microbiome structurally and functionally. 397

This approach presented a less biased and more real time and global view of Salmonella-sprout 398

interactions. 399

MATERIALS AND METHODS 400

Bacterial strain and growth condition. S. enterica serovar Cubana strain CFSAN055271 401

was obtained from the stock culture collection of the Division of Microbiology, Center for Food 402

Safety and Applied Nutrition, U.S. Food and Drug Administration, College Park, MD. It was 403

originally isolated from alfalfa sprouts in 2011 by the U.S. Department of Agriculture 404

Microbiological Data Program (MDP). Stock culture was stored in brain heart infusion (BHI) 405

broth containing 25% glycerol at -80°C and maintained on tryptic soy agar (TSA) plates. 406

Inoculation of seeds. A single colony of S. Cubana culture was transferred to 5 ml of 407

tryptic soy broth (TSB) and grown at 36°C for 18 – 20 h. The culture was harvested by 408

centrifugation at 5,000 ×g for 10 min followed by washing with 0.01M phosphate-buffered 409

saline (PBS) (pH 7.2) three times and then resuspended in 5 ml of TSB. For seed inoculum, the 410

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culture was further diluted in sterile ddH2O to four different levels (~0.2 CFU/g, ~2 CFU/g, ~104 411

CFU/g, and ~106 CFU/g seed). Alfalfa seeds (65 g or 85 g) were soaked in each 250 ml 412

inoculum for 20 min, drained and allowed to air-dry at room temperature in a Lumina hood. 413

Seeds were stored at 4°C until use. The inoculation level was determined by plate count 414

immediately following inoculation. To minimize the variations that could be introduced to the 415

experiment, the same batch of seeds was used in the study, and the seeds were inoculated, stored 416

and sprouted at the same time, respectively, for a given experiment. 417

Sprouting of alfalfa seeds. The seeds were sprouted in triplicate or quadruplicate in an 418

Easy-Sprout sprouter as follows. Twenty grams of seeds per growing vessel were soaked in 419

250 ml tap water for 8h at ambient temperature and drained. Containers were capped with 420

vented lids and incubated at ambient temperature for 4 days. The sprouting seeds were rinsed 421

every 4, 8 or 24 hours with 250 ml to 300 ml of tap water to meet varying experimental designs. 422

Sample collection and DNA extraction. In the control, 0.2 CFU/g, and 2 CFU/g 423

inoculation groups, 50 ml of sprout irrigation water from each sprouter was collected at 0h, 8h, 424

and 24h, and 20 ml was collected every 4 or 8 hours after 24h in triplicate. In the 104 CFU/g 425

inoculation group, 5 ml was collected at each corresponding time point in triplicate. After 426

collection, all samples were filtered through a MicroFunnel unit with a 0.2 um Supor® 427

membrane (Pall Corporation, Port Washington, NY). In addition, 500 ml of tap water was 428

filtered to be used as water control. After filtration, the filters were stored at −20°C prior to DNA 429

extraction. 430

Bacterial DNA was extracted with the DNeasy® PowerWater® Kit (Qiagen, Venlo, 431

Netherlands) following the manufacturer’s recommended protocol with only one modification. 432

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That is, the isolated DNA was eluted in a final volume of 50 µl. All DNA samples were stored at 433

−20°C prior to preparation of sequencing libraries. 434

Sample collection and RNA extraction. A total of 250 ml of sprout irrigation water from 435

each sprouter was collected at 0h, 24h, 48h, and 96h time points in quadruplicate at a 106 CFU/g 436

inoculation level. Bacterial cells were pelleted with a Sorvall RC 6+ centrifuge (ThermoFisher 437

Scientific, Waltham, MA) at 12,000 rpm, 4°C for 15 min using an F12S-6×500 LEX fixed angle 438

rotor. Immediately after centrifugation, the bacterial pellets were resuspended in RNAlater 439

(ThermoFisher Scientific) solution to stabilize RNA in the cells. All samples were stored at 440

−80°C prior to RNA extraction. 441

Extraction of total RNA and removal of remaining DNA was carried out using the 442

RiboPure-bacteria kit (ThermoFisher Scientific) following the manufacturer’s instructions. The 443

yield of total RNA was measured using a Qubit® fluorometer (ThermoFisher Scientific) and the 444

integrity of the RNA was verified using an Agilent Bioanalyzer 2100. 445

Library preparation and sequencing. For shotgun metagenomic sequencing, DNA 446

sample libraries were constructed with Nextera® XT library preparation kits or Nextera® DNA 447

Flex library prep kits (Illumina, Inc., San Diego, CA) following the manufacturer’s protocols. 448

Library sequencing (paired-end, 2 × 250 bp) was performed on an Illumina MiSeq (Illumina, 449

Inc.). 450

For shotgun metatranscriptome sequencing, ribosomal RNA was depleted using the Ribo-451

Zero magnetic kit for bacteria (Illumina, Inc.) following the manufacturer’s instructions. The 452

removal of rRNA was verified with an Agilent Bioanalyzer 2100, and remaining mRNA was 453

resuspended in 18 µL of Elite, Prime, Fragment (EPF) mix from the Illumina TruSeq RNA 454

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sample preparation kit v2 (Illumina, Inc.). The following cDNA synthesis and library preparation 455

were performed using the TruSeq RNA sample preparation kit v2 low sample (LS) protocol. 456

Resultant cDNA libraries were normalized, pooled and sequenced (2 × 150 bp) on one of the 457

Illumina platforms, a MiSeq or NextSeq 500 system with a high output flow cell (400M). 458

Bioinformatics analysis. For metagenomic analysis, raw sequence reads were analyzed 459

using the CosmosID bioinformatics software package (CosmosID Inc., Rockville, MD) to reveal 460

microbial community composition, antibiotic resistance markers, and virulence gene pools. For 461

metatranscriptomic analysis, raw sequence reads were annotated through CosmosID, 462

Metagenomic Phylogenetic Analysis (MetaPhlAn), BactiKmer (an in-house custom k-mer 463

database and C++ search program), and the metagenomics Rapid Annotation using Subsystem 464

Technology (MG-RAST) pipelines for taxonomic profiling. The relative abundance of each 465

bacterial organism per sample was expressed as a percentage of the total number of bacterial 466

reads belonging to that organism, normalized for organism-specific genome length. Reads 467

identified as eukaryotic, viral, or archaeal have been excluded depending on the software 468

package. For functional analysis, reads from remaining rRNA after Ribo-Zero treatment were 469

removed through SortMeRNA (65). Functional classification of transcriptional features in the 470

reads from SSIW microbiome was done based on SEED subsystem in MG-RAST (66). For 471

Salmonella differential expression analysis, the raw sequence data from NextSeq platform was 472

imported into CLC Genomic Workbench (v9) after removal of rRNA reads and mapped to the 473

annotated reference genome (CFSAN055271, which the NCBI SRA accession is SRR4175562, 474

and annotated by Prokka v1.12). The expression values for each gene and each transcript within 475

S. Cubana were calculated using the RNA-Seq analysis tool in CLC Genomic Workbench (v9). 476

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Genes differentially expressed in S. Cubana at different time points were determined using 477

EdgeR (version 3.28.0) with FDR0.05. 478

Data availability. Metagenomic and metatranscriptomic data are deposited in the NCBI 479

Sequence Read Archive (SRA) database with accession numbers SRR12284435- SRR12284466 480

(listed in Supplementary table 3). 481

482

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654

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Table 1. Summary of sequencing and annotation of Salmonella-spent sprout irrigation water metatranscriptomes. 655

656

Samples sequenced on Miseq Samples sequenced on NextSeq 500 24hCa 24hSa 48hCa 48hSa 96hCa 96hSa 0hSa 24hSa 48hSa 96hSa

Processing of raw sequences (averaged)

Total number of reads 3752463 2958984 4963610 6158197 5320040 4648767 75600887 80142881 69206224 69077254

After SortMeRNA 2899686 (77.27%)

2655703 (89.75%)

4449227 (89.64%)

5606201 (91.04%)

4206474 (79.07%)

4109479 (88.40%)

72001722 (95.24%)

67023658 (83.63%)

66010812 (95.38%)

64654545 (93.60%)

Functionally annotated transcriptional feature (from MG-RAST)

Total 461101 565145 894352 1307715 881569 1012198 30841884 16419988 42164983 30436900 Bacterial (%) 68.80 82.50 80.1 83.67 50.55 73.12 98.21 78.97 99.81 97.63 Eukaryotic (%) 2.21 3.25 0.21 0.93 0.58 0.08 0.32 1.62 0.02 0.40 Unclassified virus (%) 0.15 0.03 0.08 0.11 0.08 0.09 0.07 0.09 0.02 0.04 Unclassified bacteria (%) 0.46 0.60 0.05 0.05 0.03 0.02 0.04 0.07 0.14 0.08 Unclassified plant (%) 28.09 11.82 19.17 14.86 48.4 26.05 1.29 19.05 0.00 1.80

a Each time point consists quadruplicate samples except 24hC, 24hS, 48hC, and 0hS which only contains triplicate samples after quality control. 657

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Figure legend 659

Fig. 1 Taxonomic profiling of Spent Sprout Irrigation Water (SSIW) metagenomes. Classification was 660

performed using CosmosID software (CosmosID, Inc., Rockville, MD). Genus level taxa representing 661

more than 3% of the annotated reads are named. A) Alfalfa seeds were inoculated with S. Cubana at three 662

levels (~0.2 CFU/g, ~2 CFU/g, and ~104 CFU/g). SSIW was sampled at 0h, 8h, 24h and every 4 hours 663

after 24h in triplicate. B) Alfalfa seeds were inoculated with S. Cubana at two levels (~0.2 CFU/g, and ~2 664

CFU/g). SSIW was sampled at 0h, 8h, 24h and every 8 hours after 24h in triplicate, and tap water used to 665

irrigate the sprouts was included in duplicate. 666

Fig. 2 Taxonomic assignment of SSIW metatranscriptomes. Alfalfa seeds were inoculated with S. Cubana 667

at ~106 CFU/g seed. Enriched mRNA from SSIW at three time points (24h, 48h and 96h) were sequenced 668

and annotated using four different classification tools: (A) MetaPhlAn, (B) MG-RAST, (C) bactiKmer 669

and (D) CosmosID. Relative abundance (>0.5% cut-off) for taxa with genus level assignment is reported. 670

Fig. 3 Changes in SSIW microbial community function associated with S. Cubana seed contamination (A) 671

Functional categories of the metatranscriptomes from SSIW control community and SSIW-Salmonella 672

community. Functional classification of transcriptional features was done based on SEED subsystem. 673

Bars represent percentage of features (n=3, mean standard deviation) that were classified into the first 674

functional category level. (B) Different temporal patterns in average relative abundance observed in 675

selected most transcribed gene functions in SSIW control microbial community. (C) Changes of average 676

relative abundance in selected most transcribed gene functions across time associated with S. Cubana seed 677

contamination. (D) The second functional category levels in carbohydrate, stress response, and regulation 678

and cell signaling functions shown in average relative abundance. 679

Fig. 4 Activated genes associated with enriched biological pathways across time in response to S. Cubana 680

seed contamination. The top ten enriched KEGG pathways in SSIW metatranscriptomic dataset were 681

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identified using gene set enrichment analysis. The number of genes upregulated (red) and downregulated 682

(blue) were summarized under high level KEGG pathway category. 683

Fig. 5 Venn diagram of differentially expressed genes (DEGs) in S. Cubana from SSIW-Salmonella 684

microbial community at 24h, 48h, and 96h compared to 0h. Genes in overlapping sets show the 685

differential expression in two or three comparison pairs. 686

Fig. 6 Changes in cad and osm genes in S. Cubana from SSIW-Salmonella microbial community at 24h, 687

48h, and 96h compared to 0h. Values plotted are mean (log2) fold change in gene expression at 24h, 48h, 688

and 96h compared to 0h, respectively. Different significance levels with adjusted P values were shown as 689

***, P ≤ 0.001; **, P ≤ 0.01; *, P ≤ 0.05. 690

Fig. 7 Activated genes associated with enriched biological pathways in S. Cubana across time when 691

interacting with SSIW Microbial Community. The top ten enriched KEGG pathways in S. Cubana from 692

SSIW metatranscriptome dataset were identified using gene set enrichment analysis. (A) The number of 693

genes upregulated (red) and downregulated (blue) were summarized under high level KEGG pathway 694

category. (B) The number of genes upregulated (red) and downregulated (blue) were summarized in two-695

component system and ABC transports, specifically. 696

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